How to Prepare for
Google Professional
Data Engineer
Certification?
Google GCP-PDE Certification Made
Easy with VMExam.com.
GCP-PDE Professional Data Engineer
Certification Details
Exam Code
GCP-PDE
Full Exam Name
Google Cloud Platform - Professional Data Engineer
(GCP-PDE)
No. of Questions
50-60
Online Practice Exam Google GCP-PDE Practice Test
Sample Questions
Google GCP-PDE Sample Questions
Passing Score
Pass / Fail (Approx 70%)
Time Limit
120 minutes
Exam Fees
$200 (USD)
Become successful with VMExam.com
Google GCP-PDE Study Guide
• Perform enough practice with related
Professional Data Engineer certification on
VMExam.com.
• Understand the Exam Topics very well.
• Identify your weak areas from practice test
and do more practice with VMExam.com.
Become successful with VMExam.com
Google GCP-PDE Certification Syllabus
Syllabus Topics
● Designing data processing systems (22% of the exam)
● Ingesting and processing the data (25% of the exam)
● Storing the data (20% of the exam)
● Preparing and using data for analysis (15% of the exam)
● Maintaining and automating data workloads (18% of the exam)
Become successful with VMExam.com
Professional Data Engineer Training Details
Training:
● Google Cloud training
● Google Cloud documentation
● Google Cloud solutions
[Note: Trainings details are Given in the Description of the Video.]
Become successful with VMExam.com
Google GCP-PDE Sample
Questions
[Note: Sample Questions details are
Given in the Description of the Video.]
Become successful with VMExam.com
Que.01: Your company is loading comma-separated values
(CSV) files into BigQuery. The data is fully imported
successfully; however, the imported data is not matching byteto-byte to the source file.
What is the most likely cause of this problem?
Options:
a) The CSV data loaded in BigQuery is not flagged as CSV.
b) The CSV data had invalid rows that were skipped on import.
c) The CSV data has not gone through an ETL phase before loading into
BigQuery.
d) The CSV data loaded in BigQuery is not using BigQuery’s default
encoding.
Become successful with VMExam.com
Answer
d) The CSV data
loaded in BigQuery
is not using
BigQuery’s default
encoding.
Become successful with VMExam.com
Que.02: Your company is streaming real-time sensor data
from their factory floor into Bigtable and they have noticed
extremely poor performance.
How should the row key be redesigned to improve Bigtable
performance on queries that populate real-time dashboards?
Options:
a) Use a row key of the form <timestamp>.
b) Use a row key of the form <sensorid>.
c) Use a row key of the form <timestamp>#<sensorid>.
d) Use a row key of the form <sensorid>#<timestamp>.
Become successful with VMExam.com
Answer
d) Use a row key of
the form
<sensorid>#<timest
amp>.
Become successful with VMExam.com
Que.03: You need to stream time-series data in Avro format, and
then write this to both BigQuery and Cloud Bigtable
simultaneously using Dataflow. You want to achieve minimal endto-end latency. Your business requirements state this needs to be
completed as quickly as possible. What should you do?
Options:
a) Create a pipeline and use ParDo transform.
b) Create a pipeline that groups the data into a PCollection and uses the
Combine transform.
c) Create a pipeline that groups data using a PCollection, and then use Avro I/O
transform to write to Cloud Storage. After the data is written, load the data from
Cloud Storage into BigQuery and Bigtable.
d) Create a pipeline that groups data using a PCollection and then uses Bigtable
and BigQueryIO transforms.
Become successful with VMExam.com
Answer
d) Create a pipeline
that groups data using
a PCollection and then
uses Bigtable and
BigQueryIO
transforms.
Become successful with VMExam.com
Que.04: You are designing storage for CSV files and using an
I/O-intensive custom Apache Spark transform as part of
deploying a data pipeline on Google Cloud. You intend to use
ANSI SQL to run queries for your analysts.
How should you transform the input data?
Options:
a) Use BigQuery for storage. Use Dataflow to run the transformations.
b) Use BigQuery for storage. Use Dataproc to run the transformations.
c) Use Cloud Storage for storage. Use Dataflow to run the transformations.
d) Use Cloud Storage for storage. Use Dataproc to run the transformations.
Become successful with VMExam.com
Answer
b) Use BigQuery for
storage. Use
Dataproc to run the
transformations.
Become successful with VMExam.com
Que.05: You have 250,000 devices which produce a JSON
device status event every 10 seconds. You want to capture this
event data for outlier time series analysis. What should you do?
Options:
a) Ship the data into BigQuery. Develop a custom application that uses the
BigQuery API to query the dataset and displays device outlier data based on your
business requirements.
b) Ship the data into BigQuery. Use the BigQuery console to query the dataset and
display device outlier data based on your business requirements.
c) Ship the data into Cloud Bigtable. Use the Cloud Bigtable cbt tool to display
device outlier data based on your business requirements.
d) Ship the data into Cloud Bigtable. Install and use the HBase shell for Cloud
Bigtable to query the table for device outlier data based on your business
requirements.
Become successful with VMExam.com
Answer
c) Ship the data into
Cloud Bigtable. Use the
Cloud Bigtable cbt tool
to display device outlier
data based on your
business requirements.
Become successful with VMExam.com
Google Professional Data Engineer
Certification Guide
• The Google Certification is increasingly becoming
important for the career of employees.
• Try our Professional Data Engineer mock test.
• For more information on Google Certification please
refer to Description which is Given Below.
Become successful with VMExam.com
More Info on Google Certification
Visit www.vmexam.com
Become successful with VMExam.com